Add YOLOv26n RDK X5 model and quantized artifacts

x5_quantization/prepare_calibration.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+"""Create Horizon hb_mapper calibration feature maps from the YOLO dataset."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+
+IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
+
+
+def resize_rgb(image: Image.Image, size: int) -> np.ndarray:
+    image = image.convert("RGB")
+    resized = image.resize((size, size), Image.Resampling.BILINEAR)
+    array = np.asarray(resized, dtype=np.float32)
+    return np.transpose(array, (2, 0, 1))[None, ...]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--samples", type=int, default=128)
+    parser.add_argument("--size", type=int, default=640)
+    args = parser.parse_args()
+
+    roots = [args.dataset / "images" / "train", args.dataset / "images" / "vel"]
+    images = sorted(
+        p for root in roots if root.is_dir() for p in root.iterdir() if p.suffix.lower() in IMAGE_EXTS
+    )
+    if not images:
+        raise SystemExit(f"no images found below {args.dataset}")
+    count = min(args.samples, len(images))
+    # Evenly spread samples over the sorted set so calibration is not dominated by one split.
+    indices = np.linspace(0, len(images) - 1, count, dtype=np.int64)
+    selected = [images[int(i)] for i in indices]
+
+    args.output.mkdir(parents=True, exist_ok=True)
+    for old in args.output.iterdir():
+        if old.is_file() and old.suffix in {".bin", ".rgbchw", ".txt"}:
+            old.unlink()
+    manifest = []
+    for index, path in enumerate(selected):
+        data = resize_rgb(Image.open(path), args.size)
+        data.tofile(args.output / f"{index:05d}.rgbchw")
+        manifest.append(str(path.relative_to(args.dataset)))
+    (args.output.parent / "calibration_manifest.txt").write_text(
+        "\n".join(manifest) + "\n", encoding="utf-8"
+    )
+    print(f"wrote {count} samples ({args.size}x{args.size}) to {args.output}")
+
+
+if __name__ == "__main__":
+    main()